Abstract
With the development of RNA-seq technology and information biology, researchers have found that circRNAs are important players in many complex diseases. However, using only traditional biotechnology has the characteristics of high cost, low efficiency, and long cycle. To solve this problem, we propose a circRNA-disease associations prediction model based on graph auto-encoder and attention fusion. First, we used multiple sources of information from circRNAs and diseases to construct multidimensional similarity networks, and then integrated GCN into graph autoencoder for extracting embedded features, the embedded features were fused using the attentional fusion layer to obtain the final low-dimensional embedding, and finally, based on the proposed features, the XGBoost classifier was used to predict circRNA-disease associations, After 5-fold cross-validation, our model is superior to current advanced models, with 10 of the top 15 pairs of predicted scores for circRNA-disease associations validated by relevant databases and literature in case analysis; This demonstrates that our model can be a reliable predictive tool.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant nos. 62002189, 62102200), the Natural Science Foundation of Shandong Province, China (No. ZR2020QF038), the 20 Planned Projects in Jinan (No.2021GXRC046), and the Excellent Teaching Team Training Plan Project of QILU UNIVERSITY OF TECHNOLOGY.
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Yuan, L. et al. (2023). Identification of CircRNA-Disease Associations from the Integration of Multi-dimensional Bioinformatics with Graph Auto-encoder and Attention Fusion Model. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_8
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